Getting Started - Setup

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Introducing the datasets in this session

Apart from a COVID Cases dataset, we’ll use in-built datasets in R packages to show our examples. These include

In this session, we use datasets that are in-built into R or one of the packages above to show a single aspect.
In the group exercise we’ll use real world topical datasets and use all aspects of ggplot2 to build a complete visualizations

# 150 observations species three species of iris
?iris # use the ? to show the Help for this dataset
## starting httpd help server ... done
iris
# a set of 13 (not 12) datasets with the same summary statistics
datasaurus_dozen
# Performance (make model and stopping distance) of 32 cars from the 1970's  
mtcars
# ggplot2 provides the diamonds data set: price, cut, carat and other details of over 50,000 diamonds
diamonds
# Flights departing New York City airports in 2013, from nycflights13 package
flights 
#  ggplot2 provide the mpg dataset - fuel economy of 38 cars
mpg
# dplyr provide the starwars dataset, 87 characters for the series
starwars

Let’s look at the datasaurus dozen’s summary stats

datasaurus_dozen %>%
  group_by(dataset) %>%
  summarize(
    mean_x    = mean(x),
    mean_y    = mean(y),
    std_dev_x = sd(x),
    std_dev_y = sd(y),
    corr_x_y  = cor(x, y)
  )
covid_url <-
  "https://raw.githubusercontent.com/MarkWilcock/R-Course/main/Datasets/covid_cases_data.csv"
covid <- read_csv(covid_url)
## 
## -- Column specification --------------------------------------------------------
## cols(
##   Country = col_character(),
##   Date = col_date(format = ""),
##   DailyCases = col_double(),
##   CumulativeCases = col_double()
## )
head(covid)

Recap of ggplot2 fundamentals

ggplot2 is an intrinsic part of the tidyverse - best used with other tidyverse packages

Grammar of Graphics

The Grammar of Graphics is a key strength of ggplot2 allowing us to tailor a chart exactly to requirements

  • data - provide a dataset as a (tidyverse) tibble
  • geometries (geoms) are the basic configuration of the chart e.g. geom_line(), geom_col()
  • aesthetics map visual attributes (x, y, colour, shape, fill, alpha) to dataset columns.
  • scales configure the axes and set range, limits on visual attributes. By default, gpplot2 sets a default scale
  • stats summarize the data before plotting, most obviuosly in box plots, geom_box() but also in geom_bar()

We can have several geoms layered on a single chart e.g. line of best fit and underlying data points

The most common geoms are for line, column/bar chart, scatter plot, histograms and box and whisker charts. We’ll look at an example of each.

Variables can be discrete or continuous. Use aesthetics to map categories to colour or shape of the points.

Let’s create a few examples of charts using different geoms.

Line charts (geom_line())

Line charts often show changes over time.

Let’s see how daily COVID cases change over the last year in each country

ggplot(data = covid,
       mapping = aes(x = Date, y = DailyCases, col = Country)) +
  geom_line()

Exercise Create a line chart that shows just the England cases

Column / Bar Chart

Column charts often compare values across a set of discrete categories.

covid_by_country <-
covid %>% 
  group_by(Country) %>% 
  summarise(Cases = sum(DailyCases))

covid_by_country
ggplot(data = covid_by_country,
       mapping = aes(x = Country, y = Cases)) +
  geom_col()

co_ord_flip() changes a column chart to a “bar” chart.

ggplot(data = covid_by_country,
       mapping = aes(x = Country, y = Cases)) +
  geom_col() +
  coord_flip()

Something strange is going on below in geom_bar() that did not happen with geom_col. What is it?

ggplot(data = flights,
       mapping = aes(x = origin)) +
  geom_bar()

Let’s do it the long way

ggplot(
  data = flights %>%  group_by(origin) %>% summarise(TheCount = n()),
  mapping = aes(x = origin, y = TheCount)
) +
  geom_col()

ggplot(
  data = flights %>%  group_by(origin) %>% summarise(TheCount = n()),
  mapping = aes(x = origin, y = TheCount)
) +
  geom_bar(stat = "identity")

Exercise Create a line chart to show total cases across the four countries by date. Hint: We have just grouped the data by Country summing the DailyCases.
You may want to do something similar but grouping by Date.

#  Example answer
ggplot(
  data = covid %>%
    group_by(Date) %>%
    summarise(Cases = sum(DailyCases)),
  mapping = aes(x = Date, y = Cases)
) +
  geom_line()

## Scatter Plot (geom_point()) Scatter plots compare items across two continuous axes

Let’s compare the widths of the petals and sepals of the iris flowers

ggplot(data = iris,
       mapping = aes(x = Petal.Length, y = Sepal.Length)) +
  geom_point()

Exercise Differentiate the species by colour. An then by shape. And try mapping to alpha and size. What warnings do we get and why?

# Example answer
ggplot(data = iris,
       mapping = aes(x = Petal.Length, y = Sepal.Length, col = Species)) +
  geom_point()

Exercise Add a second layer with geom_smooth() to draw a best-fit curve, firstly with then without the confidence intervals.

# Example answer
ggplot(data = iris,
       mapping = aes(x = Petal.Length, y = Sepal.Length, col = Species)) +
  geom_point() +
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Box plot

Box plots are a visual way of seeing lots of summary statistical data in a single chart. Let’s look at the distribution of petal lengths by Species.

ggplot(data = iris,
       mapping = aes(x = Species, y = Petal.Length)) +
  geom_boxplot()

Exercise Build a box plot that compares the distribution of Sepal.Length by Species

Small multiples

Small multiple (trellis) charts show lots of small charts side by side. Each chart looks at a different aspect and is filtered on a different value of a discrete variable.

Functions * facet_wrap - when comparing over one dimension e.g. English region * facet_grid - when when comparing over two dimensions e.g. year of birth and gender

Let’s look at the COVID Cases with each Country having its own chart

ggplot(data = covid,
       mapping = aes(x = Date, y = DailyCases)) +
  geom_line() +
  facet_wrap( ~ Country)

Exercise Create small multiple scatter charts of the datasarus_dozen data where each chart shows one of the values of teh dataset column

#  Example answer
ggplot(data = datasaurus_dozen,
       mapping = aes(x = x, y = y)) +
  geom_point() +
  facet_wrap( ~ dataset) +
  xlab(NULL) +
  ylab(NULL) +
  ggtitle('The Datasaurus "Dozen"',
          "Same summary stats, very different visual representation")

Improve the look and effectiveness of our charts

Titles

Titles put the chart in context and often spell out the message the the chart designer wants to communicate. ggtitle() can show a title and sub-title - but not a very long one!

# Assign the result of the ggplot() call to a variable to we don't have to repeat so much in subsequent sections
covid_line_plot <-
  ggplot(data = covid,
         mapping = aes(x = Date, y = DailyCases, col = Country)) +
  geom_line()

covid_line_plot

covid_line_plot +
  ggtitle(
    "Cases by date reported, by nation",
    "Number of individuals who have had at least one positive COVID-19 test result"
  )

labs() is possibly a better alternative since we can label title, axes and captions in one fell swoop.

covid_line_plot +
  labs(title = "Cases by date reported, by nation",
       subtitle  = "Number of individuals who have had at least one positive COVID-19 test result",
       caption =  "Source: https://coronavirus.data.gov.uk/details/cases")

Axes

Axes describe and explain the items on the the horizontal position (x-axis) or vertical position (y-axis)

  • xlab(), ylab()
  • xlim(), ylim()
  • labs() - possibly better alternative

Legends

Functions covered: * guide_legend() * theme(legend.position = …) * scale_fill_manual(guide=guide_legend(…))

Legends explain the different categories of discrete columns or the range of continuous ones. ggplot2 provides a legend by default. We can change it position with theme(legend.position= …) We can change the look of the legend with guide_legend()

More on themes and scales later.

# Note that we start with the variable covid_line_plot
covid_line_plot +
  theme(legend.position = "bottom") +
  scale_color_discrete (guide = guide_legend(title = "UK Country"))

Exercise Move the legend to the top of the chart

Scales

Scales set the limits and ranges of the visual attributes.

scale_() functions such as scale_x_discrete() * scale_y_continuous() * scale_fill_manual() * scale_colour_discrete() * scale_colour_brewer() ???

covid_line_plot +
  scale_x_date ("Date Axis", date_labels = "%b %y") +
  scale_y_continuous("Daily Cases", limits = c(0, 60000))

Reference Lines, Annotations and Labels

Reference lines help set context - what’s good and what’s not. Annotations focus the viewer attention and help tell the story.

This section covers functions: * annotate() * geom_label() * geom_text() * geom_hline() * geom_vline() * geom_abline()

covid_line_plot +
  geom_hline(yintercept = 40000,
             col = "red",
             linetype = "dashed") +
  annotate(
    "text",
    x = min(covid$Date),
    y = 40000,
    label = "Lockdown Level",
    vjust = -0.5,
    hjust = 0
  )

Exercise Add a blue dotted line on March 23rd 2020, the start the first lockdown. Annotate it.

covid_line_plot +
  geom_vline(
    xintercept = as.Date('2020-03-20'),
    col = "blue",
    linetype = "dotted"
  ) +
  annotate(
    "text",
    x = as.Date('2020-03-20'),
    y = max(covid$DailyCases),
    label = "First lockdown starts",
    vjust = 0.5,
    hjust = -0.005
  )

Themes

Themes set the style (colours, fonts, backgrounds) for the chart as a whole in a single function. ggplot2’s default theme is rubbish so change it!

Functions covered in this session: * theme()

Try out different themes. Use the auto-complete and documentation to see the themes available. Which do you prefer?

covid_line_plot +
  theme_light()

Applying Specific Visual Attributes

Some functions allow us precisely to define visual attributes - perhaps if a theme is not exactly to our liking - or we can use these to modify a theme.

  • plot.background()
  • panel.backround()
  • panel.grid.major(), panel.grid.minor()
  • theme() with arguments plot.background, panel.background…
covid_line_plot +
  theme_light() +
  theme(panel.background = element_rect(fill="yellow"))

Exercise Build a chart with a minimal themes apart from a green panel background

ggplot2 helper packages

library(ggthemes)

ggplot2 has been so succesful and popular that others have built helper packages to extend its functionality.

ggthemes

ggthemes provides out of the box themes - e.g. minimal theme, themes to mimic certain media publications or well known software packages.

covid_line_plot +
  theme_economist()

Exercise Use the auto-complete to find you favourite theme.

ggalt

ggalt provided several geoms including geom_dumbbell() for dumbbell plots.

library(ggalt)
## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2

Lets compare the number of cases at the beginning of this year with 2 months later For this chart, we need to filter and shape the data with dplyr.

covid_start_end <-
  covid %>%
  filter(Date == '2021-01-02'  | Date == '2021-03-02') %>%
  mutate(Marker  = ifelse(Date == '2021-01-02', 'Start', 'End')) %>%
  select(Country, Marker, DailyCases) %>%
  spread(Marker, DailyCases)
ggplot(data = covid_start_end,
       mapping = aes(x = Start, xend = End, y = Country)) +
  theme_minimal() +
  geom_dumbbell(
    colour_x = "grey",
    colour_xend = "steelblue",
    size = 1,
    size_x = 5,
    size_xend = 5
  )

ggrepel reduces overlapping labels. Compare before and after

# Before
ggplot(data = mtcars,
       mapping = aes(x = disp, y = mpg)) +
  geom_point(colour = "blue", size = 2) +
  geom_text(label = rownames(mtcars)) +
  ggtitle("Without ggrepel")

library(ggrepel)
# After
ggplot(data = mtcars,
       mapping = aes(x = disp, y = mpg)) +
  geom_point(colour = "blue", size = 2) +
  geom_text_repel(label = rownames(mtcars)) +
  ggtitle("With ggrepel")
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

gganimate

gganimate changes plot over time. Is it an improvement or not? The famous example would be Hans Rosling’s motion scatter chart - wealth (x) , lifespan (y), 200 years (time), 200 countries (colour), population (size)

library(gganimate)
ggplot(
  data = filter(covid, Date > as.Date("2021-02-01")),
  mapping = aes(x = Date, y = DailyCases, col = Country)
) +
  geom_line() +
  theme_classic() +
  transition_reveal(along = Date)

patchwork

use patchwork to layout several plots

# Simplest example

library(patchwork)

cars_plot <-
  ggplot(data = mtcars,
         mapping = aes(x = disp, y = mpg)) +
  geom_point(colour = "blue", size = 2) +
  geom_text_repel(label = rownames(mtcars)) +
  ggtitle("With ggrepel")

iris_plot <-
  ggplot(data = iris,
         mapping = aes(x = Petal.Length, y = Sepal.Length, col = Species)) +
  geom_point() +
  geom_smooth(se = FALSE)

cars_plot + iris_plot
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Final Exercise

Putting it all together exercise Create a stacked bar chart of number of COVID Cases (y-axis) by date (x-axis) by country (colour) similar to that in https://coronavirus.data.gov.uk/details/cases

# Example Answer

country_colours <-
  c(
    England = "#5694ca",
    Scotland = "#003078",
    Wales = "#d4351c",
    `Northern Ireland` = "#ffdd00"
  )

ggplot(data = covid,
       mapping = aes(
         x = Date,
         y = DailyCases,
         fill = fct_rev(Country)
       )) +
  geom_col() +
  scale_fill_manual(values = country_colours, guide = guide_legend(title = NULL)) +
  theme_light() +
  ggtitle("Cases by date reported, by nation") +
  theme(legend.position = "bottom")  +
  scale_y_continuous(
    name = NULL,
    breaks = seq(10000, 70000, 10000),
    minor_breaks = NULL,
    labels = comma
  ) +
  scale_x_date(
    name = NULL,
    breaks = seq(as.Date("2020-03-01"), as.Date("2021-03-01"), by = "2 month"),
    date_labels = "%d %b"
  )

END OF SESSION